Product Assortment and Mix – Range Planning and Category Management 1. Definition of the Product Lines Change Roadmap which consisted of both

business process and IT changes. Produced a business capability model that illustrated the comparison of the As-is Solution with associated issues against the proposed To-be Solution - demonstrating the linkage to Business Process and IT Change with the resulting benefits and revenue streams.

2. Definition of the integration requirements to support the implementation of Oracle Retail Demand Forecasting (RDF/RPAS) solution. Presentation of the detailed solution architecture to Group Architecture Review Board.

Retail Sales – Multi-channel Retail Architecture – Offers and Promotions 1. Capture existing business and systems landscape overlaid with the Oracle

Retail Price Management (ORPM) scope and functionality demonstrating how ORPM will co-exist with both legacy and proposed new systems.

2. Defined visionary Business / Systems Architecture Roadmap and illustrated evolutionary change / incremental requirements delivery strategy. Included alignment to IT Policies & Standards and phased delivery of benefits stream.

3. Performed architecture analysis of the existing systems and business Designed the real-time Integration strategy and technical approach between Offers and Promotions Systems and in- store sub-systems (EPOS & SEL)

4. Produced Architecture landscape model of existing architecture overlaid with issues and new integration requirements (Sales information, Merchandising Planning, Order Management, Supply Chain, In-store, Oracle 11i Financials)

5. Produced solution designs for ORPM modifications and detailed integration requirements (e-gate/RIB, Biztalk, Ab Initio, detailed XML / ETL Schemas) and liaised with 3rd party suppliers on assessment of low level design.

6. Performed detailed analysis and design of the Promotions Operational Data Store (ODS) structure and interface requirements from ORPM to Store systems. Defined Integration and Structure of Teradata Loyalty Card DWH

7. Held Stakeholder Pre-Review Meetings for communicating the vision and solution options, socialising and building consensus, gaining commitment and buy-in for the proposed solution. Presented to the Group Review Board.

8. Defined promotions cutover strategy and plan for Products, Offers and Promotions and in- store systems (EPOS & SEL). Produced Offers and Promotions Master Plan, Roadmap and Solution Architecture Blueprint.

9. Identified required modifications to: Oracle Sales Audit System (ORSA), Real Time Sales (RTS) from Store, in-store EPOS and SEL (Tills and Printing).

10. Socialised Master Plan and Business / Systems Architecture Roadmap to key Business and Technology Community Stakeholders. Presented Sales and Merchandising Architecture Models and Road Map to Programme Board

Relevant Experience

• Global SAP implementations - HANA IS/Retail ECC6 Financials

• SAP Architecture and Design Work-stream management

• Digital Process re-engineering and Business Transformation

• SAP solution design - Global Templates & Design Patterns,

Functional Expertise

Professional Background

Mr Tebbutt is a Digital Architect @ UK Digital Partners – driving the Digital Customer Experience and Journey. He has a deep and broad experience across the Retail Sector –Business Process, Retail IS/IT Architecture Digital Technology perspectives with strong Social Media and Big Data exposure Mr Tebbutt has 5 years expertise in Multi-channel Retailing– with 10 years SAP ECC6 experience (including 2 years SAP HANA / 5 years in SAP Finance, Planning and Strategy roles) 7 years of Retail Merchandising - Oracle Retail @ Argos, bhs & Tesco + SAP IS/Retail roll-outs @ BP, Orange & Somerfield His effective role is Digital Strategist in Multi-channel Retail environments - working with the business to ensure fit-for-purpose Digital Marketing / Multi-channel Retail capability.

Most Recent Role

Huawei – The Huawei SmartCare platform is the first Cloud-based Telco 2.0 in-a-box solution to be driven by Big Data – covering Network (OSS) Customer (BSS) and Shared Services (ESS). Virgin Mobile is the first UK Telco Service Provider for SmartCare. SmartCare is a solution for lean All-IP network operation which helps operators to build a network O&M system that can expose core network services, visualise the All-IP network, identify the causes of network problems, analyse subscriber behaviour and reflect true subscriber experience

Name: Nigel Tebbutt

• 7 years experience in Retail (3 years in Oracle Retail roles, 4 years Multi-channel Retailing)

• 10 years experience SAP ECC6 (including 2 years SAP HANA and 5 years in SAP IS/Retail Finance, Planning and Strategy with 3 SAP IS/Retail roll-outs.)

Industry Experience

Insert

Photo

CAREER SUMMARY

Name: Nigel Tebbutt

• Multi-channel Retail Architecture – e-Commerce Platforms

• In-home / In-store (F2F) • Catalogue • Call Centre • Internet • • Mobile Apps • Social Media Channels • Secure Mobile Payments • • Stebo • ATG Dynamo • IBM WepSphere e-Commerce Servers • • Oracle Retail / CRM • SAP IS/Retail / CRM • Manugistics • Quantum

• Enterprise Services Layer – Real-time Analytics @ POS • Real-time Analytics at POS / Digital Mapping and Spatial Analysis • Service Aggregation, Intelligent Agents and Alerts • Data Analysis, Data Mining and Statistical Analysis • Facial, Image and Wave-form Analytics, Pattern and Trend Analysis

• Mobile Enterprise Platforms – Cloud Computing on the move • Smart Devices – Laptops, Tablets, PDA’s, Smart Phones • Smart Apps – Mobile Applications for Smart Devices • In-store Wireless Smart Grid – deployed via Wireless Hubs and Femtocells

• Next-Generation Network (NGN) Telecommunications Architectures – JAIN •. IP v.6 •. SIP •. IMS •. • Femtocells – 3G UMTS / GPRS and 4G HSDPA •.Wireless Hubs - WiFi / WiMAX, and LTE • Mobile Devices •. Smart Apps •. Apps Shops • J2EE for mobile •.Mobile Desktop •. mVoIP •.

MULTI-CHANNEL RETAIL ARCHITECTURE and the MOBILE ENTERPRISE

1. Retail 2.0 “Perfect Store” Business Operating Model (BOM) • Next Generation Enterprise (NGE) Retail Strategy • Real-time Analytics at

Point-of-Sale. Transforming the “Perfect Store” Strategy into the Retail 2.0 Proposition - Store Tier / Clustering and Localisation 2. Multi-channel Retail Architecture – In-store (F2F), Product Catalogue, Call Centre, Internet, Mobile (Smart Devices, Smart Grid, Smart

Apps, Cloud Services) Social Media 3. Category Management - Product Assortment and Mix and Shelf / Space Planning. Inventory Planning and Forecasting, Inventory,

Provisioning and Replenishment 4. Customer Centric Retailing - “Customer First” – Using Insight to drive and optimise customer satisfaction, loyalty and revenue •

Customer Loyalty and Reward Systems • 1. Customer Offer – Product Assortment and Mix v. Store Tier/Cluster • Contact channels, media & intermediaries 2. Customer Journey - planning the customer journey via Customer Insight and Up-sell / Cross-sell Campaigns 3. Customer Experience Management – ensuring a rewarding and satisfying Customer Interaction Experience 4. Multi-channel Retail - ensuring consistency, quality and performance across all contact channels, intermediaries and media 5. Customer Segmentation - Customer Profiling, Streaming and Segmentation, 6. Social Media Strategy – Social Media Channels, Social Mapping, Conversations, Social CRM 7. Customer Loyalty - Business Intelligence, DWH/BI, Analytics • Product Lifecycle / Master Data Management 8. Offers and Promotions - Campaign Planning & Management Up-sell / Cross-sell • Real-time Analytics at Point-of-Sale • 9. Customer Insights –`Geo-demographics, Ethnographic & Social Anthropology, Epidemiology, Morbidity Actuarial Science 10. CRM Strategy •. Customer Relationship Management •.Customer Experience Management • Customer Information Management •.

Customer Insight and Loyalty – Planning the Customer Offer, Experience and Journey

Retail 2.0 “Perfect Store” – Experience

RETAIL DOMAINS EXPERIENCE – “Perfect Store”

RETAIL 2.0

DOMAINS

BUY MOVE SELL Planning

and

Forecasting

Procure Provision

Replenish

Logistics Customer

Management

Channels Marketing Retail

Operations

Head Office

Future

Management

Strategic

Foresight and

Future Studies

Sustainability

Renewable

Resources

Future Logistics

Landscape

Social Anthropology

Ethno-graphics

Demographics

Future PDA Hand

Held Device and

Smart Device

Propositions

Future Retail

Markets and

Opportunities,

Future Retail

Landscape

Future Retail

Policy and

Legislation

Strategy and

Planning

Store Tiers /

Clustering

Product

Assortment and

Mix

Vendor

Management

Strategy

Category

Management

Strategy

RFID

Wireless

GPRS / UMTS

/ WAP

Hand Held Device

and PDA

Customer Insight

and Loyalty

Strategy

Mass

Customisation

Micro-marketing

Channels Strategy

MVNO / MVPN

Propositions

Smart Devices -

Planning and

Transition

Retail

Proposition and

Customer

Offer,

Customer

Experience and

Journey,

Governance,

Reporting and

Controls

IFRS

SOX

Business

Operations

Planning and

Demand

Forecasting

Contracts and

Framework

Agreements

Purchasing

Schedules and

Call-off

Inventory and

Provisioning

Logistics

Operations

Value Chain

Management

Customer

Management

Business Operating

Model (BOM)

Channels Business

Operating Model

(BOM)

Offers and

Promotions

Management

Product /

Category

Management

Retail Operations

Business

Operating Model

(BOM

Value Chain

Management

Retail

Performance

Reporting, and

Management

DWH

BI

Analytics

Architecture Planning and

Forecasting

Architecture

Vendor

Management

and

Procurement

Architecture

Inventory,

Provisioning

and

Replenishment

Architecture

Supply Chain,

Architecture

Customer Domain

Architecture

Channel

Architecture

PLCM / CRM

Architecture

EPOS / Retail

Merchandising

Architecture

Financials,

Reporting and

Analytics

Architecture

Solution

Architecture

Planning and

Forecasting

Solutions Design

Procurement

Solution Design

Inventory,

Provisioning

and

Replenishment

Solution Design

Supply Chain,

Solution Design

CRM Systems

Call Centre and

Contact Centre

Solution Design

Channel; Access

Solution Design

PIMS and

Campaign

Management

Architectures

EPOS / Retail

Merchandising

Solution Design

Performance

Management

DWH and BI

Systems

Management

Planning and

Forecasting

Systems

Manugistics,

Quantum

Procure-to-Pay

Systems

JDA Retail

Oracle Retail

SAP IS / Retail

Provisioning

Systems

JDA Retail

Oracle Retail

SAP IS / Retail

GIS Mapping and

Network Gazetteer

Supply Chain

Systems

CRM Systems

Call Centre and

Contact Centre

Systems

Content

Management

e-commerce

Systems

PIMS / CRM

and Campaign

Systems

JDA Retail

Oracle Retail

SAP IS / Retail

EPOS / Retail

Salas Systems

and CRM

Systems

Record-to-Report

Systems

JDA Retail

Oracle Retail

SAP IS / Retail

IBM FileNet, EDM

Infrastructure

Management

Retail

Infrastructure

Monitoring and

Control

Warehouse and

Distribution

Automation

Multi-media

Channel Access

and Fulfilment

Multi-media

Channel Access

and Fulfilment

Business Continuity

On-demand

Computing and

Shared

Services

EPOS Network

Infrastructure

Monitoring and

Control

Desktop Services

Client Inventory,

Provisioning, Help

Desk and

Support

Key Basic Industry Sector Familiarity /

Understanding

Good Segment Understanding / Previous

Experience

Current Segment / Business Unit Knowledge

Disruptive Digital Business Transformation

• To create a Digital Enterprise, strategic Business Transformation needs re-designed People,

Process and Technology orchestration and collaboration with Digital Technology infrastructure.

Digital disruption and service innovation demands an agile corporate culture which can support

rapid and efficient design, deployment and launch of Digital USPs – digital customer solutions

that support. service differentiation and demonstrate customer excellence in the increasingly

competitive digital marketplace. Digital systems drive the customer journey and showcase novel

digital features and functions which can deliver a compelling customer experience and journey

• In order to meet the challenges of Digital Business Transformation, the implementation of

efficient new business models and rapid adoption of novel and emerging digital technology

capabilities is an essential prerequisite for making business sense of the torrent of digital data

being captured from internal and external data streams. Big Data Analytics and Data Science

provides the platform to integrate, aggregate and correlate the data streams flowing from every

digital device and Social Media, Smart Apps / Devices, Mobile Platforms and Cloud Services –

providing actionable insights into customer needs and behaviour , so turning Data Streams into

Revenue Streams – to achieve operational business targets and deliver key strategic outcomes.

Retail 2.0 Digital Transformation

Part 2

Part 4

Part 3

Part 1

Strategic Enterprise Management Framework

Enterprise Target Operating Model (eTOM)

Future Management and Innovation Plans

Solution Architecture

Enterprise Architecture Model and Roadmap

Enterprise Architecture

Business Programme Plan / Project Plans

Infrastructure Architecture

Business Operating Model (BOM)

Business Architecture

Strategic Outcomes, Goals & Objectives

Innovation, Research and Development

Business Programme Management

IS / IT Strategy

Technology Strategy

Systems Planning

Enterprise Governance, Reporting and Controls

Infrastructure Planning

Business Planning

Organisation Structure

Retail 1.0 Strategic Foresight

Strategy Development

Organisational Change

Enterprise Architecture Framework

NGE – Next-

Generation

Enterprises

Collaborative

Business

Models

Service

Convergence I

Business Transformation

Technology Change

NGA- Next-

Generation

Architectures

Enterprise

Application

Integration

Technology

Convergence I

Buy Move Sell

Smart

Devices

Mobile

Platform

Cloud

Services Retail 2.0

I

I

Transition - Retail 1.0 to Retail 2.0 “Perfect Store” Business Operating Model = Innovation I

FAST FASHION RETAILING and BRAND MANAGEMENT

In Europe, consumer spending is being re-focussed on either Value Brands or Luxury Goods Marques - squeezing out Retailers with mid-market Retail Propositions and traditional middle-of-the-road Branding Strategies. Traditional Fashion Retailers have seasons – Spring / Summer and Autumn / Winter - where popular lines are retained year-on-year. Fast Fashion Retailers (where Fast Fashion lines are only in-store for a few days or weeks, and Fast Fashion items are not subsequently repeated) are growing fast - at the expense of those conventional retailers with traditional Spring / Summer and Autumn / Winter Seasons which often feature “signature” popular repeatable core lines - always available, season on season, year on year..... Fast Fashion and Luxury Goods Retailers are now under intense competitive pressure to drive down costs by adopting a more Lean / Agile Supply Chain Model (a la mode de Wal-Mart), and by improving Supplier Relationships and Strategic Vendor Management. Fast Fashion Retailers are also required to be better at exploiting On-line and Mobile Sales Channels - which are growing much faster than traditional In-store and Catalogue Channels. Customers still like to mix-and-match Sales Channels - unwanted items purchased On-line are often exchanged In-store for replacement or refunds. Consumers are becoming increasingly better educated. Across many urban conurbations in the Southern part of the UK, young people purchase cheap fashion items frequently and in large numbers - these items are worn for a single season (or until they fall apart.....) and are viewed by consumers almost as disposable items. Young consumers with similar disposable incomes in major Cities in Scotland and Northern Italy, for example - will spend the same amount in a season on just a few items chosen very carefully from Luxury Goods Brands - but keep them in their wardrobe for many years..... The sudden proliferation of pervasive Smart Devices communicating via the Smart Grid with the Cloud indicates that we may have just witnessed the beginning of a startling new episode in technology driven consumer behaviour – the advent of the always-on digital connected society – Smart individuals living in Smart households within the Smart Cities of the future. Smart Phones such as the Apple iPhone, HTC Desire, Google Nexus One, Windows Phones – are enabling innovative Customer Experience and Journey Stories, both in-store and mobile, including Social Media Conversations.. The fastest growing sales Channels for both Fast Fashion and Luxury Goods are Smart Apps on Mobile Phones. Innovative new Retail Business Operating Models such as “Retail 2.0” and “Perfect Store” are driving the development of these new Channels. For example, when a Customer enters a store, the Retailer of the Future can detect and identify him from his Smart Phone Number, as the Customer accesses the In-store WiFi or WiMAX Network Connection. Based on vast amounts of data describing their previous consumer behaviour – we can alert the consumer to relevant In-store offers and promotions – based on Propensity Modelling –similar in content and style to those offers and promotions the customer has responded to positively in the past When a Customer Tweets that she is going to buy a “little black cocktail dress” – we can initiate a Social Media Conversation . Retail 2.0 and Perfect Store Business Operating Models and Customer Experience and Journey Business Value Propositions are being driven by technology enablement such as Multi-channel Retail (eCRM), and Social Media (sCRM), supported by Real-time Analytics @ Point-of-Sale: -

• Retail Business Models – “Retail 2.0” • “Perfect Store” • • Retail Strategy – Retail Proposition • Channels • Media • • Business Value Propositions – Customer Offer, Experience and Journey • • Mobile Technologies – Mobile Computing • Smart Devices • Smart Apps • • Customer Strategy – Customer Loyalty • Offers • Promotions • Campaigns • • Retail Business Transformation – New Social Structures • Cultural Change • • Emerging Technologies – Real-time Analytics @ POS • Smart Grid • Cloud Services • Social Marketing – Internet Intelligence • Product Placement • Crowd Sourcing Events • Fulfilment – Service Access • Service Brokering • Service Provisioning • Service Delivery

Retail 2.0 “Perfect Store” – Experience

Retail 2.0 “Perfect Store” – Process Architecture

PS0004

Shelf / Space Allocation

PS0001

Customer Offer

PS0002

Retail Proposition

PS0003

Pricing

PS0019

Marketing Communications

(Advertise)

PS0012

Customer Profiling &

Segmentation

PS0009

Global CRM

PS0011 Marketing Services -

(Research and Analysis)

PS0010

Customer Experience and

Journey

PS0006

Product Assortment and

Mix

PS0008

Forecasting and Replenishment

PS0007

Global Category & Supplier

PS0021

Sales Analysis and Value Chain

Reporting

PS0022

Global Product Sourcing

PS0023

Global Supply Chain

PS0014

BUY

(Procurement)

PS0016

SELL Retail

Merchandising

PS0015

MOVE

(Logistics)

PS0017

Public Relations

PS0024

Global Shared Services

PS0005

Business Planning &

Optimisation

PS00029

Analytics

PS0027

Social Intelligence

PS0028

Digital Platforms & Multi-channel

Retail

Digital Channels & Analytics

Retail Merchandising & Logistics Head Office

Customer Relationship Management

PS0018

Customer Information &

Services

PS0013

Customer Loyalty

Schemes

Customer Services

PS0025

Global Product Catalogue

PS0020,

Offers and Promotions

PS0026

Local Product Management

LUXURY GOODS RETAILING and BRAND MANAGEMENT

Luxury Goods companies have traditionally targeted two primary “old money” customer segments – affluent fashion-conscious socialites (age range 25-35) who follow the skiing, sailing and social seasons in major cities and exclusive resorts in either Europe or America - and retired or semi-retired individuals (age range 55-65) who have created and accumulated significant wealth during their Business and Professional careers– and who now have significant time and money available to devote towards their interests and leisure pursuits. Families are raised in the Gap Years (age range 35-55). Many familiar Luxury Goods brands now belong to just a few Luxury Brand Aggregators such as French PPR, Louis Vuiton Moet Hennessy (LVMH) and the Swiss conglomerate Richemont. In any economic downturn, these Brand Aggregators are no longer able to drive increased growth sufficient to meet their Shareholder expectations or maintain volume targets from Business Partner / Stakeholders, in traditional Markets and Customer Segments – and so are forced to expand their Market Coverage, Product Ranges and Brand Footprints (and at the same time risk suffering the dual unforeseen consequences of erosion of Product positioning, desirability and cache – along with the dilution of core Brand recognition, perception and value). Today, the new Luxury Goods marketing focus has turned towards two “new money” customer segments - newly wealthy individuals in the emerging economies of the BRICS;s (Brazil, Russia, India and China) – and young Media and Entertainment Professionals and Elite Team Sports Athletes (age range 20-30) in the West. Goldman Sachs forecast that China will be buying one 3rd of the world's luxury goods in under a decade,,,,,

• Young Media and Entertainment Professionals and Elite Team Sports Athletes (age range 20-30) • New, Emerging and Developing Markets for Luxury Goods– Brazil, Russia, India China (the BRICs) •

Increasingly, many Luxury Brands are also launching more accessible entry-level Product Ranges in order to attract younger, technically-savvy and fashion-aware mass-market consumers - to introduce them to a Lifestyle Experience and Journey that creates brand loyalty and lock-in with entry-level Luxury Goods Product ranges. As these young, mobile consumers careers develop and they begin to generate increased disposable income they also begin to purchase "big-ticket" Luxury Goods items from their favourite Design Guru or Lifestyle Icon.....

• Mass-market younger, technically-savvy and fashion-aware consumers • Entry-level Luxury Goods Product Ranges – Perfume, Cosmetics, Casual Wear, Sporting Goods

Retail 2.0 and Perfect Store Business Operating Models and Customer Experience and Journey Business Value Propositions are being driven by technology enablement such as Multi-channel Retail (eCRM), and Social Media (sCRM), supported by Real-time Analytics @ Point-of-Sale: -

• A winning Customer Contact Strategy to reach out to your target audience • A stunning Customer Experience to engage and retain your target audience • Understanding of Customer Profiling and Segmentation - to define your niche • A unique Customer Offer and Journey to instil desire for your Ranges and Lines • An enthralling Customer Experience to cultivate Consumer aspiration and desire • An amazing Customer Journey Storyboard to grasp and keep Consumer attention • A compelling Retail Proposition / Channels / Media to leverage Customer interest • A mastery of Smart Devices • Smart Apps • Cloud Services to engage your Customer • Total perfection of Product and Service Delivery Management for Consumer Fulfilment • Influencer Programmes - the ability to turn Fashion Blogs into Revenue – and so transform Clicks into Cash.....

Retail 2.0 “Perfect Store” – Experience

Retail 2.0 “Perfect Store” – Multi-channel Architecture

SAP Hybris or IBM

WebSphere

SAP NetWeaver Pi and/ or IBM MQSI

SAP IS/Retail

SAP CRM

Stebo or IBM Product Centre

Internet

Contact Centre

Mobile 3rd Party

SAP Solution Architecture

Customer Loyalty

EPOS / SEL

Sales Channels Fulfilment Channels

In-store

Home Delivery

BI / BO / BW HANA

SAP ECC7, ERP

ATG Dynamo Oracle Fusion Oracle Retail

Oracle CRM

Stebo or Kalido

Internet

Contact Centre

Mobile 3rd Party

Oracle Solution Architecture

Customer Loyalty

EPOS

Sales Channels

Fulfilment Channels

In-store

Home Delivery

Oracle OBIE

Oracle e-Business Suite

E-commerce Platform

Integration Platform

Retail Platform

CRM Platform

Catalogue Platform

Internet

Contact Centre

Mobile 3rd Party

Customer Loyalty

In-store Systems

Sales Channels Fulfilment Channels

In-store

Home Delivery

Retail 2.0 “Perfect Store” Multi-channel Enterprise Architecture

Data Warehouse

Head Office Shared

Services

Social Media Real-time Analytics

Mobile Platforms

Cloud Digital Channels Social Media Conversations

Multi-channel Retail Architecture

Multi-channel Retail

Retail Operations – Retail Merchandising and Logistics

Head Office – Finance, Planning and Strategy

Marketing – Customer Loyalty, Experience and Journey – Offers, Promotions and Campaigns

In-store EPOS – Internet – Home Delivery

Provisioning & Replenishment

In-store Systems

Retail Operations Systems

ERP Systems

Customers

Operations Managers

Finance Managers

Loyalty Mart

Financial Data Warehouse

CRM and Marketing Systems

Marketing Managers

Multi-channel

Sales Data Warehouse

Marketing

Customer Analytics Reports

Retail

Multi-channel Sales

Analysis

Operations Warehousing &

Logistics Reports

Head Office

Financial Analysis Reports

e-Commerce Systems

Campaign Mart

Merchandising & Logistics Data

Supplier Data

Product Data

Stores Data

Merchandising

Inventory & Provisioning

Reports

EPOS Data

Call Centre Data

Internet Data

Customer DWH

CRM Data

Retail Managers

ERP Data

Catalogue Systems

Planning & Forecasting

Systems

“BIG DATA”

Retail and Logistics Data

Warehouse

Planning & Forecasting

Systems

Apache Hadoop Framework

HDFS, MapReduce, MetLab, “R”

Catalogue Data

Autonomy, Vertical

Hadoop

SAP HANA

• Case Study 1 – BT SmartReach – BT is one of the UK’s largest corporations, and has

partnered with Detica and Aqiva to form SmartReach to partner with energy utility Scottish

Power and Siemens Energy – in order to trial Smart Metering and bid for DECC / OFGEM

Advanced Meter Infrastructure (AMI) Smart Meter energy data management contracts.

• Case Study 2 – Kingfisher Group Future Homes – Kingfisher Group launched Future Homes

as a Special Purpose Vehicle to bring to market and launch new Smart Energy Supply, Green

Deal and Eco Funding Energy Products and Mobile Services hosted in the Cloud. Key

features of this novel market proposition include the cloud deployment of SalesForce.com.

• Case Study 3 – Audience Metrics & Analytics - actionable insights from audience data,

Due to severe competition, Communications Service Providers (CSPs) such as 3 Mobile,

EE, Talk-Talk and Vodafone, along with Mobile Virtual Network Operators (MVNOs) such as

Virgin, Tesco and Giff-gaff - no longer make significant profit from their core services (Mobile,

Fixed-line and Broadband). This has caused the dash for Quad-play, where CSPs add Media

and Entertainment Packages to their core network services (Mobile, Fixed-line & Broadband).

TV Set-top Boxes (Sky, Virgin, EE) are connected to the Internet and continuously stream

Audience Channel Selection data and Music Play-lists to Communications Service Provider

(CSP) Audience Insight and Analytics servers. Similarly, Smart Phone Apps (BBC i-player,

Sky Go, Netflix, Spotify) also continuously stream Audience Channel Selection data and Music

Play-lists to the Communications Service Provider (CSP) via tools like Apigee.

Case Studies Summary – Cloud / Mobile Digital Transformation

• Case Study 4 – Huawei SmartCare Customer Experience Management (CEM) Solution is

the first Cloud-based Telco 2.0 in-a-box Customer Relationship Management (CRM) and

Customer Experience Management (CEM) system to be driven and informed by Big Data

Analytics – covering Network (OSS) Customer (BSS) and Shared Services (ESS) Telco

Domains. Virgin Mobile is the first UK Telco Service Provider on SmartCare. SmartCare is a

solution for lean All-IP network operation which helps operators to build network API services

that can visualise the All-IP network in order to identify the causes of network problems and

analyse subscriber behaviour to enhance and enrich the subscriber digital journey / experience

• The Huawei SmartCare Cloud System platform is based on the real-time harvesting of billions

of Call Details Records (CDRs) – both Financial and Network CDRs (which are usually lost in the

Mediation-Rating-Billing process) – records which are now stored and analysed using “Big Data”

Analytics techniques. Network CDRs give us both intimate insights into Subscriber Behaviour

and Network Performance. Typically, poor Customer Service and Dropped / Lost Calls are

reasons for Subscriber Churn. Customers experiencing excessive Cellular Network issues are

identified using Causal Layer Analysis (CLA) - and Network Fault records are raised.

Case Studies Summary – Cloud / Mobile Digital Transformation

• Case Study 5 – Telefónica Digital was created as a Special Purpose Vehicle to lead

Telefónica’s transformation into an M2M / M2C / C2C Digital Services provider - cloud

computing / digital telecommunications value added network services (VANS). Telefónica

Digital is the vehicle for launch / bringing to market digital products and services - which

will help to improve the lives of customers by leveraging the power of digital technology.

This ranges from developing new technologies for healthcare providers to communicate

with other stakeholders, to helping Healthcare Providers, Life Sciences businesses and

government Health Departments to discover actionable clinical insights, address new

opportunities, streamline operations, improve efficiency and increase performance.

• Case Study 6 – HP Autonomy Medical Analytics. Changing healthcare service

provisioning, regulation and patient demographics are putting increasing pressure on the

healthcare industry to make significant improvements in care quality, cost management,

organizational efficiency and compliance. Priorities include the need to address topical

and challenging issues such as missed diagnosis, misdiagnosis, delayed diagnosis,

coding error, over / under treatment, inappropriate or unnecessary clinical procedures,

drugs and medications, insurance fraud, lack of preventive screening and / or proactive

health maintenance. Improved collaboration within the organization with joined-up

processes, better information sharing, and a holistic approach to capture and action

medical insights across the organization - are crucial to Digital Healthcare success.

Case Studies Summary – Cloud / Mobile Digital Transformation

Case Study 1 – BT SmartReach

• BT is one of the UK’s largest corporations, and has partnered with Detica and Arqiva to form SmartReach – in order to work with energy utility Scottish Power and Siemens Energy to explore the practicalities of smart metering and smart grids. The initial pilot began this year (2012) in Suffolk with 1,000 Smart Meter installations rolled out by Energy Provider Scottish Power and Smart Meter Operators, Siemens Metering Services (SMS) – a UK division of Siemens Energy.

• BT, along with Detica and Arquiva, are driving forward the SmartReach Demonstration Project, located in Ipswich. This Smart metering scheme has 1,000 customers located across Suffolk towns including Ipswich, Woodbridge, Hintlesham and Martlesham.

• "It is about providing customers with information, technology and processes to manage their carbon footprint and improve home energy efficiency” – says Chris Amos., Director at BT SmartReach.

• "BT SmartReach will be able to demonstrate how Smart Metering, the Smart Grid and other new and emerging technologies can improve the reliability of power delivery and provide customers with greater control over their carbon footprint and energy consumption.“

Use your Smart Phone.....

.....to manage your Smart Home !

CASE STUDY 1: – SMART METER and SMART GRID

• Case Study 2 – Kingfisher Group Future Homes – Kingfisher Group launched Future Homes

as a Special Purpose Vehicle to bring to market and launch new Smart Energy Supply, Green

Deal and Eco Funding Energy Products and Mobile Services hosted in the Cloud. Key features

of this novel market proposition include the cloud deployment of AWS and SalesForce.com.

Future Home Design possibilities and variations are endless. Here are some examples: -

Future Home - example scenario and use case – “I’m Home”

• A Future Home scenario such as the “I’m Home” use case may be triggered by vehicle

proximity identification or via a Smart Phone App – or manually from your vehicle as you

approach the driveway by pressing a single button on a key-ring remote-control. The building

automation control system receives, identifies and validates the key-ring remote-control's

command and executes the “I’m Home” use case.

• This triggers a pre-programmed sequence of function events - for example starting by turning on

the lighting in the driveway, garage, hallway and kitchen whilst opening the garage doors. It

then disarms the security system, opens the garage door, unlocks the interior garage entry

door, adjusts the waste heat air exchange system or air-conditioning to a pre-set ambient

temperature, and turns on the home entertainment system playing your favourite Movie, Audio

Playlist or Channel selection - whilst making you a coffee and drawing you a bath.....

CASE STUDY 2: – Kingfisher Future Homes

SalesForce.com – Architecture Overview

SalesForce.com Customer Relationship

Management (CRM)

Service Provisioning and Service Management

Contract Management and Product Warranty

Customer Requirements

Supplier Products, Services and Skills

Databases

Mobile Smart Devices / Apps

Cloud Services Data Centre

ERP

CRM Sales and Marketing

PROSPECTS

ORDERS

PROPOSALS & QUOTES

CONTRACTS

CUSTOMERS

Financials

Finance, Accounting and Supplier Management

Business Services - Provisioning and Management Systems

CASE STUDY 2: – Kingfisher Future Homes

Sales and Marketing

Mobile Platform

CASE STUDY 3: – Audience Metrics & Analytics - actionable insights from audience data

• Due to severe competition, Communications Service Providers (CSPs) such as 3 Mobile,

EE, Talk-Talk and Vodafone, along with Mobile Virtual Network Operators (MVNOs) such as

Virgin, Tesco and Giff-gaff - no longer make significant profit from their core services (Mobile,

Fixed-line and Broadband). This has caused the dash for Quad-play, where CSPs add Media

and Entertainment Packages to their core network services (Mobile, Fixed-line & Broadband).

• TV Set-top Boxes (Sky, Virgin, EE) are connected to the Internet and continuously stream

Audience Channel Selection data and Music Play-lists to the Communications Service

Provider (CSP) Audience Insight and Analytics servers. Similarly, Smart Phone Apps (BBC i-

player, Sky Go, Netflix, Spotify) also continuously stream Audience Channel Selection data

and Music Play-lists to the Communications Service Provider (CSP) via tools such as Apigee.

• In a typical household (Mother, Father, two children) there may be four Smart Phones and as

many as ten other internet connected devices (Tablets, Laptops, Internet TVs, TV Set-top

Boxes and Video Games Boxes) – all streaming video, audio and data – the details of which

are captured, stored and analysed by the Communications Service Provider (CSP) using “Big

Data” Analytics techniques. This yields valuable Audience Metrics and Analytics based on

intimate understanding of consumer video, audio and internet content from which actionable

audience insights is derived from video, audio and internet streaming data – which drives

Personalised Advertising across all devices (Smart Phone, Tablet, Internet TV, Games Boxes).

CASE STUDY 3: – Audience Metrics and Lifestyle Demographics

CASE STUDY 3: – Audience Metrics and Lifestyle Demographics

The Cone™ - Audience Understanding

Customer Management (CRM / CEM)

Social Intelligence

Campaign Management

e-Business

Big Data Analytics

The Cone™

Customer Loyalty

& Brand Affinity

The Cone™

Smart Apps

CASE STUDY 3: – Audience Metrics and Lifestyle Demographics

CASE STUDY 3: – Audience Metrics & Analytics - actionable insights from audience data

• The principle of The Cone™ Audience Metrics & Analytics Solution is firstly to understand

people’s lives, and then understand the role that different entertainment concepts and content

play in their lives. Using this narrative of understanding, we can gain unique insights, helping

make better and more incisive decisions through understanding who ideas are connecting with

and why that inspires creative marketing. We then apply The Cone™ creative inspiration to

innovate compelling propositions and ideas that will connect with the widest possible audiences.

• On the surface, The Cone™ profiles people’s propensity to engage with any given lens e.g. film,

reality TV, music, radio, mobile, etc. along our FECI continuum: ranging from Fanatics through

Enthusiasts to Casuals and “Indifferent” – finally the “Unconnected”. We then use proprietary

data analytics to profile and describe groups of similar people within the FECI continuum.

• The Cone™ facilitates our understanding of how groups of like-minded individuals are

connecting (or not connecting…..) with our brand and content – thus we can use intimate

personal insights to learn how to inspire the right kinds of ideas and events to better target brand

positioning and product content, influencing more receptive audiences, so delivering new core

fan connections which drives an expanding and increasingly loyal fan base …..

CASE STUDY 3: – Audience Metrics and Lifestyle Demographics

Social Intelligence

Cloud CRM

Data

Profile

Data CRM / CEM

Big Data

Analytics

Customer Management (CRM / CEM)

Social Intelligence

Campaign Management e-Business

Big Data Analytics

The Cone™

Customer Loyalty

& Brand Affinity

The Cone™ Smart Apps

Audience Survey Data

Insights

Reports

TV Set-top Box

CASE STUDY 4: – Huawei SmartCare CEM

• Huawei SmartCare CEM is based on the real-time harvesting of billions of Call Details Records

(CDRs) – both Financial and Network CDRs (which are usually lost in the Mediation-Rating-

Billing process) – are now stored and analysed using “Big Data” Analytics techniques.

• Network CDRs give us both intimate insights into Subscriber Behaviour and Network

Performance. Typically, poor Customer Service and Dropped / Lost Calls are reasons for

Subscriber Churn. Customers experiencing excessive Cellular Network issues are identified

using Causal Layer Analysis (CLA) - and Network Fault records are raised.

• A typical household (Mother, Father, two children) may have four Smart Phones and up to ten

other internet connected devices (Tablets, Laptops, Internet TVs, TV Set-top Boxes and Video

Games Boxes). The very high local levels of internet traffic in a typical home may be the cause

lost calls and dropped connections due to Cell Channel contention. The solution is to supply a

small local cell network (Femtocell) in the home, connected directly to the Optical Internet hub –

giving up to eight additional WiFi / 4G Channels for public, private or mixed consumer access.

• Similarly, Femtocells may be supplied and fitted in the workplace – giving businesses their own

local small cell network providing up to hundreds of additional WiFi / 4G Cellular Channels.

CASE STUDY 4: – Huawei SmartCare CEM

Case Study 4 – Huawei SmartCare CEM

Customers

Campaign Mart

Analytics & Customer

Loyalty

Loyalty Mart

CRM Data

Customer DWH Customer Care “BIG DATA”

Merchandising & Logistics Data

Retail Data Warehouse

Retail

Multi-channel Sales Analysis

Mobile Platforms

EPOS Data

Call Centre Data

Internet Data

e-Commerce Systems

Store Systems

Merchandising

Warehousing & Logistics

Inventory & Provisioning

Hadoop Cluster

SAP HANA

ERP Systems

Finance Managers

Financial Data Warehouse

Head Office Financial

Analysis Reports

ERP Data

OSS – Network Management

Network Provisioning & Fault Management

Operations Network Data

Network and Fault Reports

Operations Managers

Inventory, Provisioning & Replenishment

BSS – Rating, Mediation and Billing

Mediation Rating and

Billing Systems

Business Managers

Supplier Data

Product Data

Customer Data

Inventory & Provisioning

Reports

Planning & Forecasting

Systems

CDR Data

Call Data Warehouse

Billing Data

Autonomy Vertica

Operational “BIG DATA”

Multi-channel Retail

MSS – Head Office – Finance, Planning &Strategy

Social Media - External Data

Customer Care Systems

CRM & Digital Marketing Systems

Customers

CEM

SAP HANA

Catalogue

Hadoop Cluster Pentaho, MetLab, “R”

Clouders

Apache Hadoop

Framework

CASE STUDY 5: – Digital Healthcare in the Cloud

• Digital Healthcare is a cluster of new and emerging applications and technologies that exploit digital, mobile, analytic and cloud platforms for treating and supporting patients. Digital Healthcare is necessarily generic as this novel and exciting Digital Healthcare innovation approach is being applied to a very wide range of social and health problems, ranging from monitoring patients in intensive care, general wards, in convalescence or at home – to helping doctors make better and more accurate diagnoses, improving drugs prescription and referral decisions for clinical treatment.

• Digital Healthcare has evolved from the need for more proactive and efficient healthcare delivery, and seeks to offer new types of prevention and care at reduced cost – using methods that are only possible thanks to sophisticated technology.

• Telefónica Digital is sponsoring research into Smart Wards with St. Thomas's Hospital in London. At the Institute of Digital Healthcare, part of the Science City Research Alliance, researchers are not only looking to develop new technologies, but to base this firmly on a pragmatic understanding of both the benefits and limitations of integration with commercial Digital Healthcare products which are currently on offer.

CASE STUDY 5: – Digital Healthcare

CASE STUDY 6: – HP Autonomy Medical Analytics - actionable insights from clinical data

• HP Healthcare Analytics delivers a robust and integrated set of core and healthcare industry

specific capabilities which organises and interprets unstructured data in context - designed to

harness this untapped clinical data and unlock actionable medical insights. This helps to

improve care quality by connecting healthcare providers directly with their data through self-

service analytics; providing intelligence for more accurate diagnoses so reducing errors, risk

and unnecessary treatments; enabling better understanding of how delivery affects outcomes

and uncovering insights for preventive measures to decrease the rate of avoidable diseases.

• Changing demographics and regulations are putting tremendous pressure on the healthcare

industry to make significant improvements in care quality, cost management, organizational

efficiency and compliance. To stay viable, it is paramount to effectively address issues such as

misdiagnosis, coding error, over/under treatment, unnecessary procedures and medications,

fraud, delayed diagnosis, lack of preventive screening and proactive health maintenance. To

that end, better collaboration within the organization with improved information sharing, and a

holistic approach to capture actionable insights across the organization becomes crucial.

• In an environment prevalent with multiple unstructured data silos and traditional analytics

focused on structured data, healthcare organizations struggle to harness 90%* of their core

data - which is mostly medical images, biomedical data streams and unstructured free text

found in clinical notes across multiple operational domains. This rich and rapidly growing data

asset containing significant biomedical intelligence is exploited using HP Medical Analytics,.

CASE STUDY 6: – Medical Analytics

Telematics The Internet of Things (IoT) – Smart Devices, Smart Apps, Wearable Technology, Vehicle Telemetry, Smart Homes and Building Automation

SMACT/4D Digital Technologies

SMACT/4D OVERVIEW

• While Telematics, Social, Mobile, Analytics and Cloud technologies add a new

dimension to the Digital 2.0 business operating model and technology landscape, to

fully maximize their value - consider the whole to be greater than sum of its parts.....

• The formula for the Future of Work is centred around T-SMAC – Telematics, Social,

Mobile, Analytics and Cloud – totally integrated on a single technology stack, where

every function enables all of the others to maximize their cumulative impact. This is

the foundation of a new Enterprise Architecture model delivering Digital Technology

that supports an organization that is fully integrated in real-time – and is thus more

lean, agile, effective, connected, collaborative, productive and customer-focussed.

T-SMAC – Telematics, Social, Mobile, Analytics and Cloud

• Telematics – the Internet of Things (IoT)

• Social Media / User Content / Virtual Communities / Digital Ecosystems

• Mobile Communication Platforms / Smart Devices / Smart Apps

• Analytics / 4D Geospatial Data Science / Big Data / Hadoop / SSDs / GPUs

• Cloud Services Platforms

SMACT/4D – Telematics, Social, Mobile, Analytics, Cloud

SMACT/4D – Telematics, Social, Mobile, Analytics, Cloud. Telemetry and GIS

• Today’s SMACT/4D Stack™ - ‘the fifth wave’ of IT architecture - is happening faster

and with greater impact than any other disruptive technology that has ever come

before. By 2020, as many as 30 billion fixed devices will be connected to the internet

and 70 billion mobile computing devices will be connected to the Cloud. Enterprises

will be managing 50 times the amount of data than they do currently. So SMACT/4D

Stack™ will have a multiplying effect on businesses and increase productivity across

the organization – whilst placing a massive burden on Service Providers of future

Digital Communications Technology Stacks, Platforms and Architectures.

The SMACT/4D Stack™ Effect

• In all Industries across the business landscape, the SMACT/4D Stack™ is eroding

the century-old blueprint of value chains and spawning new, highly distributed, digital

business models, social networks, virtual communities and digital ecosystems. The

power of SMACT/4D Stack™ technology platforms is released by treating SMACT

4D Stack™ as an integrated digital stack – as core components combine to create a

massive multiplying effect when they are integrated and deployed together.

SMACT/4D – Telematics, Social, Mobile, Analytics, Cloud

• Increasing change is rapidly driving customer, businesses and technology interaction in a tight

embrace, with the convergence of disruptive technologies eroding the boundaries separating them.

Businesses are becoming more and more agile, and technologies such as social media, mobility,

analytics and cloud computing are coming together to unleash unlimited opportunities for everyone

involved. This convergence – also known as SMAC – will be the leading disruptor to the business-

technology ecosystem over the next few years.

SMACT/4D Digital Technologies

Telematics

• Telematics is an interdisciplinary field of Digital Communication Technology (DCT) for

the long-distance transmission and processing of automatic (machine generated) digital

information (telemetry). While this application might suggest a much more universally

encompassing definition than Machine-generated / Automatic Data Streams between

Smart Devices and the Cloud - it is simply the branch of T-SMAC Digital technology

which deals with the Internet of Things (IoT) – the management of remote devices via

mobile telecommunications and cloud platforms.

• Telematics – pervasive Fixed / Mobile Internet-connected Smart Devices delivering

Machine-generated / Automatic Digital Data and Video Streams - Mobile-to-Mobile (M2M)

and Mobile-to-Cloud (M2C) – the Internet of Things (IoT) Typical Telematics Data

Sources might include: -

– Geophysical data from remote devices in Digital Oilfields

– Image Data from satellites, aircraft and drones in Digital Battlefields

– Wearable Technology – digital data streaming from wearable devices

– Environment data from remote oceanographic buoys and weather stations

– Vehicle Telemetry from spacecraft, aircraft, ships, trains and road transport

– Image Data from vehicles, aircraft and drones with Emergency Response Teams

SMACT/4D Digital Technologies

MOBILE ENTERPRISE (MEAP’s) - Vendors & Technologies

Social Media / User Content

• A social media strategy has become a must for all enterprises, be it banks, retailers or

the government. With over one billion individuals logged on to various social networks,

people are now using social media for advice on what products to buy, where to shop

and even regarding what firms they want to work with. While most enterprises use

social media for their customer service function only, many firms have now started

using social media in tandem with their sales and marketing functions. This in turn

enables firms to use data generated by the customers effectively to service their larger

pools of customers.

Mobility

• Mobile devices have changed the way people access digital content. Smartphones and

tablets have brought rich, digital content to the fingertips of consumers. Mobile banking

has emerged as one of the most innovative products in the financial services industry.

Shoppers are increasingly using their mobile devices for everything from browsing to

comparing to buying products. Governments are also reaching out to their citizens,

using mobile devices as an efficient channel. Enterprises must also jump on to the

mobility bandwagon, and ensure that their applications are mobile ready.

SMACT/4D Digital Technologies

Chart showing the growth of Smart-phones as compared to PCs. This remarkable trend has got all of the PC manufacturers worried - they are all looking into transitioning into the manufacture of Smart-phones, PDAs and Tablets. Now is the time to enter the Digital Enterprise and Mobile Platform marketplace - before its too late,,,,,

Digital Enterprise and Mobile Platform Growth Curve

Analytics

• Every year, companies and individuals generate billions of gigabytes of data - which

properly analyzed and used in real-time, can provide distinct competitive advantage.

Enterprises need to recognize the opportunity that analytics represents and should adapt

their IT strategy to capture such opportunities’. Analytics can help retailers predict buying

decisions of shoppers; it can help banks weed out fraudulent transactions; while

governments can use analytics to provide services directly to their citizens. Predictive

analytics has also been adopted across industries in various scenario building activities.

Cloud computing

• The undeniable power of cloud computing to foster innovations and imprve productivity is

now accepted by both IT vendors and their customers. While the financial services and

government sectors are mostly moving to a private cloud model due to information

security concerns, other industries like healthcare and retail have adopted public cloud.

Moreover, their existing infrastructure has helped telecom players to emerge as providers

of cloud computing, leading to erosion in boundaries between IT and telecom vendors.

SMACT/4D Digital Technologies

• Telematics is an interdisciplinary field of Digital Communication Technology (DCT) for the

long-distance transmission and processing of automatic (machine generated) digital

information (telemetry). While this application might suggest a much more universally

encompassing definition than Machine-generated / Automatic Data Streams between Smart

Devices and the Cloud - it is simply the branch of T-SMAC Digital technology which deals

with the Internet of Things (IoT) – the management of remote devices via mobile

telecommunications and cloud platforms. Telematics – pervasive Fixed / Mobile Internet-

connected Smart Devices delivering Machine-generated / Automatic Digital Data and Video

Streams - Mobile-to-Mobile (M2M) and Mobile-to-Cloud (M2C) – the Internet of Things (IoT)

• 4D Geospatial Analytics is the profiling and analysis of large aggregated datasets in order

to determine a ‘natural’ structure of groupings provides an important technique for many

statistical and analytic applications. Demographic and Geospatial Cluster Analysis - on

the basis of profile similarities or geographic distribution - is a statistical method whereby no

prior assumptions are made concerning the number of groups or group hierarchies and

internal data structures. Geo-spatial and geodemographic techniques are frequently used in

order to profile and segment populations by ‘natural’ groupings - such as common

behavioural traits, Clinical Trial, Morbidity or Actuarial outcomes - along with many other

shared characteristics and common factors.....

SMACT/4D Digital Technologies

Social Media and User Content is the fastest growing category of user-provided global content and will

eventually grow to 20% of all internet content. Gartner defines social media content as unstructured

data created, edited and published by users on external platforms including Facebook, MySpace,

LinkedIn, Twitter, Xing, YouTube and a myriad of other social networking platforms - in addition to

internal Corporate Wikis, special interest group blogs, communications and collaboration platforms.....

Social Mapping is the method used to describe how social linkage between individuals define Social

Networks and to understand the nature of intimate relationships between individuals.

SOCIAL MEDIA – The pattern of Social Relationships between people.....

• Using “BIG DATA” to drive Market Sentiment •

Unprompted online conversations, statements and news create an online reflection of real-life events and issues – influencing the thoughts of individual consumers – managing Reputational Risk and so shaping Market Sentiment. The Social Media data, Blogs and News feeds that form this digital mirror of the world provides a gold mine of actionable information.....

SOCIAL INTELLIGENCE – Social Graphs and Market Sentiment

• Influencer Programmes have a long history in

industries such as software, computers and

electronics, - but today they are successfully

deployed across all types of industries including

automotive, smart phones, fashion, health and

nutrition, wine, sports, music, technology, travel

tourism and leisure – and financial services.....

• In a hyper-connected world market-makers and

influencers increasingly provide the gateway to

decision makers who drive consumer behaviour.

• Unprompted online conversations, statements and

news create an online reflection of real-life events

and issues – influencing the thoughts of individual

consumers and so shaping Market Sentiment.

• The Social Media data and News feeds that form

this digital mirror of the world provides a gold mine

of information. However, unlocking the data is not

straight forward as it requires a complex and

unique set of technologies, skills and methods.....

INFLUENCER PROGRAMMES – Social Media Conversations

INFLUENCER PROGRAMMES – Social Media Conversations

Traditional CRM was very much based around data and information that brands could collect on their customers, all of which would go into a CRM system that then allowed the company to better target various customers. CRM is comprised of sales, marketing and service /

support–based functions whose purpose was to move the customer through a pipeline with the goal of keeping the customer coming back to buy more and more stuff......

TRADITIONAL CRM – Customer Management Pipeline

Evolution of CRM to SCRM - The challenge for organizations now is adapting and evolving to meet the needs and demands of these new social customers - many organizations still

do not understand the CRM value of social media.....

SOCIAL CRM – Social Media Conversations

In Social CRM - the customer is actually the focal point of how an organization operates. Instead of marketing products or pushing messages to customers, brands now talk to and collaborate with their customers to solve business problems, empower customers to shape their own Customer

Experience and Journeys and develop strong customer relationships - which will over time, turn participants into brand evangelists and positive customer advocates.....

SOCIAL CRM – Social CRM Processes

Posted on April 20, 2010 by Laurance Buchanan - Capgemini

SOCIAL CRM – a Business Framework and Operating Model

Social CRM - a Business Framework and Operating Model

SalesForce.com – a Cloud Platform CRM / CEM Business Solution

The Cone™ - Lifestyle Understanding

Customer Management (CRM / CEM)

Social Intelligence

Campaign Management

e-Business

Big Data Analytics

The Cone™

Customer Loyalty

& Brand Affinity

The Cone™

Smart Apps

The Cone™ – Digital Marketing

Data Streams into Revenue Streams…..

• Digital Marketing is the communication, advertising and marketing of brands, products and services via multiple digital channels and channel partners in order to reach out to, contact and connect, on the most intimate terms, with the widest possible range of consumers. Through the exploitation of Digital Media we can initiate and maintain engaging Social Conversations.

• Digital Marketing extends key Brand Messages across every digital platform, from simple internet marketing to mobile, broadcast and social media channels – yielding Social Intelligence data in order to discover actionable Marketing Insights – which in turn convert digital Data Streams into Revenue Streams

• The key objective of Digital Marketing is to reach out to, contact and connect directly with carefully selected consumers – so that we create strong, lasting and durable relationships in order to promote key brand, category and product messages to targeted consumers and thus develop a tangible, valuable. very real and distinct brand / category / product interest, following, affinity and loyalty

Social Intelligence – Profiling and Analysis

Fanatics - 10%

Enthusiasts - 20%

Casuals - 30%

Indifferent - 40%

The Cone™ – Profiling & Analysis

The Cone™ Brand Loyalty & Affinity

The Cone™ - Eight Primitives

Primitive Problem / Opportunity Business

Domain

System Function Software Product

Who ? Who are our Customers ? Party - People /

Organisations

CRM / CEM SalesForce.com -

Customer Management

What ? What are they saying

about us ?

Social Media /

Communications

Social Intelligence Google Analytics,

Anomaly 42

Why ? Why - their Interest /

Behaviour / Motivation /

Aspirations / Desires ?

Brand Identity /

Loyalty / Affinity /

Offers / Promos’

Marketing,

Campaign

Management

Predictive Analytics /

Propensity Modelling

Where ? Where do they Live /

Work / Shop / Relax ?

Places -

Location

GIS / GPS Geospatial Analytics

When ? When do they contact /

buy products from us ?

Time / Date Sales Transaction Multi-channel Retail /

Mobile Platforms

How ? How do they contact and

connect with us – Media /

Telecoms Channels ?

Communications

Channel

• Mobile

• Internet

• In-store

Multi-channel Retail /

Mobile Platforms

Which ? Which Brands / Ranges /

Categories / Products ?

Retail

Merchandising

Product

Catalogue

IBM Product Centre /

Stebo / Kalido

Via ? Via Business Partners /

3rd Party Channels ?

Sales Channel Retail Channel /

Outlet

Amazon, E-bay, Alibaba

Event Dimension

Party Dimension

Geographic Dimension

Motivation Dimension

Time Dimension

Media Dimension

Cone™ MEDIA FACT

WHO ? WHAT ? WHERE ?

HOW ? WHEN ? WHY ?

• Indifferent • Casuals

• Enthusiasts • Fanatics

• Radio Show • Television Show • Internet Advert

• Campaign • Offer

• Promotion

• Pre-order • Purchase • Download

• Playlist

• Booking • Attendance

• Advert / Publicity • Posting / Blog

• Facebook • LinkedIn • Myspace • Twitter

• YouTube • Xing

• Region / Country • State / County

• City / Town • Street / Building

• Postcode

• Person • Organisation

Product Dimension

WHICH ?

• Category • Label / Artist

• Album / Track • Tour / City / Arena

• Merchandise

Channel Dimension

VIA ?

• Channel / Partner • In-store

• Internet Service • Mobile Smart App

(Spotify etc.)

Advert / Publicity Type

Sales Channel

Posting / Blog Source / Type

Subject

Location

Media

Event

• Awareness • Interest

• Need • Desire

Motivation

Customer

Time / Date

Version 2 – Media Co’s

The Cone™ - Eight Primitives

Social Intelligence – Streaming and Segmentation

Social Interaction

Brand Affinity

Geo-demographic Profile

Experian Mosaic – 15 Groups (Streams), 66 Types (Segments)

Hybrid Cone – 3 Dimensions The Cone™ – Streaming & Segmentation

The Cone™ Brand Loyalty & Affinity

The Cone™ - Converting Data Streams into Revenue Streams

Salesforce

Anomaly 42

Cone

Unica

End User

BIG DATA

ANALYTICS

SOCIAL MEDIA

E-Commerce Platform

FULFILMENT Sales Orders

The Cone™ Brand Loyalty & Affinity

SalesForce CRM

Geo-demographics • Streaming

• Segmentation • Household Data

SOCIAL CRM Households

Insights

Insights Insights

Anomaly 42 Unica

Offers and Promotions

People and Places

Campaigns

Social Intelligence • User Content and Blogs

• Social Groups and Networks

EXPERIAN

Social Intelligence – Actionable Insights

Brand Affinity

Social Interaction

Geo-demographic Profile

Experian Mosaic – 15 Groups (Segments), 66 Types (Streams)

Hybrid Cone – 3 Dimensions

Fanatics - 10%

Enthusiasts - 20%

Casuals - 30%

Indifferent - 40%

The Cone™ Brand Loyalty & Affinity

The Cone™ – Actionable Insights

Social Analytics – Split-Map-Shuffle-Reduce Process

Split Map Shuffle Reduce

Key / Value Pairs

The Cone™ - CAMPAIGN

Social Intelligence – CAMPAIGN MANAGEMENT

The Cone™ – CONSUMER CYCLE

Salesforce

Anomaly 42

Cone

Unica

End User

BIG DATA

ANALYTICS

Cone™ Brand Affinity

Campaign

CRM

Insights

Insights Insights

SALES

PEOPLE

DEMOGRAPHICS Household Data

SOCIAL INTELLIGENCE User Content, Social Groups and Networks

Offers and Promotions

People & Places

Streaming & Segmentation

The Cone™ – CONSUMER CYCLE

Case Study – Huawei SmartCare CEM

Customers

Campaign Mart

Analytics & Customer

Loyalty

Loyalty Mart

CRM Data

Customer DWH Customer Care “BIG DATA”

Merchandising & Logistics Data

Retail Data Warehouse

Retail

Multi-channel Sales Analysis

Mobile Platforms

EPOS Data

Call Centre Data

Internet Data

e-Commerce Systems

Store Systems

Merchandising

Warehousing & Logistics

Inventory & Provisioning

Hadoop Cluster

SAP HANA

ERP Systems

Finance Managers

Financial Data Warehouse

Head Office Financial

Analysis Reports

ERP Data

OSS – Network Management

Network Provisioning & Fault Management

Operations Network Data

Network and Fault Reports

Operations Managers

Inventory, Provisioning & Replenishment

BSS – Rating, Mediation and Billing

Mediation Rating and

Billing Systems

Business Managers

Supplier Data

Product Data

Customer Data

Inventory & Provisioning

Reports

Planning & Forecasting

Systems

CDR Data

Call Data Warehouse

Billing Data

Autonomy Vertica

Operational “BIG DATA”

Multi-channel Retail

MSS – Head Office – Finance, Planning &Strategy

Social Media - External Data

Customer Care Systems

CRM & Digital Marketing Systems

Customers

CEM

SAP HANA

Catalogue

Hadoop Cluster Pentaho, MetLab, “R”

Cloudera

Apache Hadoop

Framework

Geo-demographics - “Big Data”

• 4D Geospatial Analytics is the

profiling and analysis of large

aggregated datasets in order to

determine a ‘natural’ structure of

groupings provides an important

technique for many statistical and

analytic applications.

• Demographic and Geospatial

Cluster Analysis - on the basis of

profile similarities or geographic

distribution - is a statistical method

whereby no prior assumptions are

made concerning the number of

groups or group hierarchies and

internal structure. Geo-spatial and

geodemographic techniques are

frequently used in order to profile

and segment populations by

‘natural’ groupings - such as

common behavioural traits, Clinical

Trial, Morbidity or Actuarial

outcomes - along with many other

shared characteristics and

common factors.....

Targeting – Map / Reduce

Consume – End-User Data

Data Acquisition – High-Volume Data Flows

– Mobile Enterprise Platforms (MEAP’s)

Apache Hadoop Framework HDFS, MapReduce, Metlab “R” Autonomy, Vertica

Smart Devices Smart Apps Smart Grid

Clinical Trial, Morbidity and Actuarial Outcomes Market Sentiment and Price Curve Forecasting Horizon Scanning,, Tracking and Monitoring Weak Signal, Wild Card and Black Swan Event Forecasting

– Data Delivery and Consumption

News Feeds and Digital Media Global Internet Content Social Mapping Social Media Social CRM

– Data Discovery and Collection

– Analytics Engines - Hadoop

– Data Presentation and Display Excel Web Mobile

– Data Management Processes

Data Audit Data Profile Data Quality Reporting Data Quality Improvement Data Extract, Transform, Load

– Performance Acceleration

GPU’s – massive parallelism SSD’s – in-memory processing DBMS – ultra-fast data replication

– Data Management Tools DataFlux Embarcadero Informatica Talend

– Info. Management Tools Business Objects Cognos Hyperion Microstrategy

Biolap Jedox Sagent Polaris

Teradata SAP HANA Netezza (now IBM) Greenplum (now EMC2) Extreme Data xdg Zybert Gridbox

– Data Warehouse Appliances

Ab Initio Ascential Genio Orchestra

Social Intelligence – The Emerging Big Data Stack

• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration

of Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic)

context. The problems encountered in exploring and analysing vast volumes of spatial–

temporal information in today's data-rich landscape – are becoming increasingly difficult to

manage effectively. In order to overcome the problem of data volume and scale in a Time

(history) and Space (location) context requires not only traditional location–space and

attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the

additional dimension of time–space analysis. The Temporal Wave supports a new method

of Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.

• This time-visualisation approach integrates Geospatial (location) data within a Temporal

(timeline) dataset - along with data visualisation techniques - thus improving accessibility,

exploration and analysis of the huge amounts of geo-spatial data used to support geo-visual

“Big Data” analytics. The temporal wave combines the strengths of both linear timeline and

cyclical wave-form analysis – and is able to represent data both within a Time (history) and

Space (geographic) context simultaneously – and even at different levels of granularity.

Linear and cyclic trends in space-time data may be represented in combination with other

graphic representations typical for location–space and attribute–space data-types. The

Temporal Wave can be used in roles as a time–space data reference system, as a time–

space continuum representation tool, and as time–space interaction tool.

4D Geospatial Analytics – The Temporal Wave

Social Intelligence – Brand Affinity

CONE SEGMENTS - BRAND AFFINITY

• Social Intelligence drives Brand Loyalty Understanding - Fan-base Profiling, Streaming and Segmentation – expressed in the creation and maintenance of a detailed History and Balanced Scorecard for every individual in the Cone, allowing summation by Stream / Segment: -

1. Inactive – need to draw their attention towards the Brand

2. Indifferent – need to educate them about core Brand Values

3. Disconnected– need to re-engage with the Brand

4. Casuals – exhibit Brand awareness and interest

5. Followers – follow the Brand, engage with social media and consume brand communications

6. Enthusiasts – engaged with the Brand, participate in Brand / Product / Media events and merchandising

7. Supporters– show strong need, desire and propensity to support Brand / Product / Media consumption

8. Fanatics – demonstrate total Commitment / Dedication / Loyalty for all aspects of the Brand / Product / Media

PROPENSITY

• Balanced Scorecard – is a summary of all the data-points for an Individual / Stream / Segment

• Propensity Score – In the statistical analysis of observational data, Propensity Score Matching (PSM) is a statistical matching technique that attempts to estimate the effect of a Campaign / Offer / Promotion or other intervention by calculating the impact of factors that predict the outcome of the Campaign / Offer / Promotion.

• Propensity Model – is the Baysian probability of the outcome of an event in an Individual / Stream / Segment

• Predictive Analytics - an area of data mining that deals with extracting information from data and using it to predict trends and behaviour patterns. Often the unknown event of interest is in the future, however, Predictive Analytics can be applied to any type of event with an unknown outcome - in the past, present or future.

Social Intelligence – Fan-base Understanding

Social Intelligence – Fan-base Understanding

CONE STREAMING and SEGMENTATION

• Multiple Cones can be created and cross-referenced using Social Intelligence and Brand

Interaction / Fan-base Profiling and Segmentation in order to deliver actionable insights for any

genre of Brand Loyalty and Fan-base Understanding – as well as for other Geo-demographic

Analytics purposes – e.g. Digital Healthcare, Clinical Trials, Morbidity and Actuarial Outcomes: -

– Music (e.g. BBC and Sony Music)

– Broadcasting (e.g. Radio 1 / American Idol)

– Digital Media Content (e.g. Sony Films / Netflix)

– Sports Franchises (e.g. Manchester City / New York City)

– Sport Footwear and Apparel (e.g. Nike, Puma, Adidas, Reebok)

– Fast Fashion Retailers (e.g. ASOS, Next, New Look, Primark)

– Luxury Brands / Aggregators (e.g. Armani, Burberry, Versace / LVMH, PPR, Richemont)

– Multi-channel Retailers – Brand Affinity / Loyalty Marketing + Product Campaigns, Offers & Promotions

– Financial Services Companies – Brand Protection and Reputation Management

– Travel, Leisure and Entertainment Organisations - Destination Events and Resorts

– MVNO / CSPs - OTT Business Partner Analytics (Sky Go, Netflix, iPlayer via Firebrand / Apigee)

– Telco, Media and Communications - Churn Management / Conquest / Up-sell / Cross-sell Campaigns

– Digital Healthcare – Private / Public Healthcare Service Provisioning: - Geo-demographic Clustering and

Propensity Modelling (Patient Monitoring, Wellbeing, Clinical Trials, Morbidity and Actuarial Outcomes)

Social Intelligence – Fan-base Understanding

Social Intelligence – Social Interaction

Social Interaction Cone Rules

1. Inactive – not engaged – low evidence / low affinity / low interest in Social Media

2. Lone Wolf – sparse / thin social network - may share negative information (Trolling)

3. Home Boy – Social Network clustered around Home Location Postcodes (Gang Culture)

4. Eternal Student – Social Network clustered around School / College / University Alumni

5. Workplace – Social Network clustered around Work and Colleagues (e.g. City Brokers, Traders)

6. Friends and Family – Social Network clustered around physical social contacts - Friends and Family

7. Enthusiast – Social Network clustered around shared, common interests – Sport. Music and Fashion etc.

8. Promiscuous – Open Networker – virtual Social Network across all categories- will connect with anybody

Number of Segments

• With anonymous data (e.g polls) then the number of initial Segments is 4 (Matt Holland). With named

individuals we can discover much richer internal and external data sources (Social Media / User Content /

Experian) - and therefore segment the population with greater granularity

Individuals Qualifying for Multiple Segments.

• When individuals qualify for multiple segments - we can either add these deviant individuals to the

Segment that they have the greatest affinity with - or kick out any such deviants into an Outlying / Outcast

/ Miscellaneous Segment for further processing or manual intervention

Social Interaction

How consumers use social media (e.g., Facebook, Twitter) to address and/or engage with companies around social and environmental issues.

Multi-channel Retail - Digital Architecture

• The last decade has seen an unprecedented explosion in mobile platforms as the internet

and mobile worlds came of age. It is no longer acceptable to have only a bricks-and-mortar

high-street presence – customer-focused companies are now expected to deliver their

Customer Experience and Journey via internet websites, mobiles and more recently tablets.

TELCO 2.0

DOMAINS

Operational Support Systems Business Support Systems Support Systems

Environment

Management

Network Smart and

Hand Held

Devices

Retail Customer

Management

Telco Billing

Rating and

Mediation

Marketing Settlement Head Office

Future

Management

Sustainability

Renewable

Resources

NGN - Next

Generation

Network

Architectures

4G / Edge

Future Handset

PDA and Hand

Held Devices

Smart Device

Propositions

Future Telco

Retail Model and

Landscape

Social Anthropology

Ethno-graphics

Demographics

Telco Consolidation

and Convergence

ETOM

Future Telco

Markets and

Landscape

Future Telco

Interconnect

Wholesale

Contracts and

Agreements

Strategic Foresight

and Future

Management

Future Telco

Policy and

Legislation

Strategy and

Planning

Hydroelectricity

Solar, Wind and

Tidal Power

Geothermal

Energy

Bio-fuels

Future Shared

Network

Planning

IMS / SIP

Cloud

Computing

MVNO / VPN

Propositions

Smart Metering

-Planning and

Transition

Electronic Toll

& Congestion

Mgt.

Telco Retail

Proposition and

Customer Offer

Product / Service

Packaging and

Development

Customer Offer,

Experience and

Journey Planning

Micro-marketing and

Mass-customisation

Fixed-to-Mobile

Convergence - FMC

BSS / ESS

Convergence - SDP

Mediation Rating

and Telco Billing IS /

IT Planning and

Strategy

Customer

Insight &

Loyalty

Strategy

Customer

Profiling,

Streaming and

Segmentation

Risk Management

Frameworks

- Outsights

- COSO

Governance,

Reporting &

Controls

- IFRS

- COBIT

- SOX

Business

Operations

Micro-Generation

CHP Combined

Heat & Power

Civil Engineering

Environment

Management

Inventory

Provisioning

Work

Scheduling

Job

Management

Smart Metering

and IDEX

Energy Data

Management

Electronic

Traffic

Management

Retail Operations

Value Chain

Management

Customer

Relationship

Management

Business Operating

Model (CRM BOM)

Mediation, Rating

and Telco Billing

Business Operating

Model (BOM)

Product / Tariff

Management

Campaign

Management

Contracts and

Settlements

Balancing, &

Optimisation

Performance

Managements

DWH / BI

Analytics

Data

Mining

Architecture Asset and

Environment

Management

Architecture

Network

Infrastructure

Architecture

Smart Meter

Infrastructure

Architecture

MVNO / VPN

Platforms

Supply Chain,

EPOS, Retail

Merchandising

Architecture

Customer Domain

Architecture

Customer Profiling,

Streaming and

Segmentation

Mediation Rating and

Telco Billing

Architecture

PLCM / CRM

Architecture

Contracts and

Settlements

Architecture

Financials and

Settlements

Document

Management

Solution

Architecture

Asset and

Environment

Management

Solution Design

Network

Infrastructure

Management

Solution Design

Smart Meter

Information

Management

MVNO / VPN

Solution Design

Supply Chain ,

EPOS, Retail

Merchandising

Solution Design

Contact Centre

Solution Design

Mediation Rating and

Telco g Billing

Solution Design

PIMS / CRM

Contact and

Campaign

Management

Solution Design

Contracts and

Settlements

Management

Solution Design

Performance

Management

DWH and BI

Architecture

Systems

Management

Plant, Building,

Site and

Environment

Management

Systems

GIS Mapping

and Network

Gazetteer

Network

Monitoring &

Control

Systems

Energy Data

Collection and

Aggregation

Systems -

IDEX

MVNO / VPN

Meter Network

Management

Supply Chain

EPOS / Retail

Systems and

CRM Systems

Contact Centre and

Customer Systems

– Oracle CRM

– SAP CRM

– Unica /

Cognos

– Clarity

– Onyx

Telco Billing and

Collection Systems

– Oracle BRM

– SingleView

– Amdocs

– Keenan

PIMS Systems

CRM Systems

Campaign

Management

Systems

Contracts and

Settlements

Management

Systems

Oracle e-business

Suite, BRM, CRM

SAP IS Retail, Ent.

Portal, MDM, Pi, FI

CO SD BPEM,

SEM, SSM. BI and

BW

IBM FileNet, ECM

Infrastructur

e

Management

Telco Network

Infrastructure

Telco Network

Monitoring and

Control

Network

Security

Anti-trafficking

and Counter-

terrorist

measures

Smart Device

Infrastructure

Management

Standardised

Terminating

Equipment

Business

Continuity

Disaster

Recovery

EPOS Network

Multi-media Channel

Access and

Fulfilment

Avaya, Genesys,

Nortel Switches

Multi-media Channel

Access and

Fulfilment

Document Print

Management

Diallers / Routers

On-demand

Computing and

Shared

Services

VR IVR /

Diallers

Cisco Routers

Virtualisation,

Automation

On-demand

Computing and

Shared Services

Desktop Services

Client Inventory,

Provisioning, Help

Desk and Support

Business

Continuity

Telco 2.0 Business and Technology Domains

Unified Communications

Unified Communications Unified Communications is the integration of real-time communication services - such as unified messaging, rich presence, security and identity access information, telephony, video streaming, conferencing, desktop sharing, data sharing, call monitoring and control, speech recognition - with real-time and non-real-time communication services - such as instant messaging

Unified Communications

Unified Communications With so many ideas and definitions of Unified Communications (UC), it is often difficult to determine the value stream that UC delivers to businesses. However, managing the volume and priority of e-mails, voicemails, SMS texts, telephone calls and instant messages that the average person reads, composes, sends and receives during the working day - it becomes clear the abundance of information propels employees into a much faster, more challenging and dynamic environment.

Unified Communications

Unified Communications – Service Management

Name: Nigel Tebbutt

“DATA SCIENCE” – my own special area of Business expertise

Targeting – Map / Reduce

Consume – End-User Data

Data Acquisition – High-Volume Data Flows

– Mobile Enterprise Platforms (MEAP’s)

Apache Hadoop Framework HDFS, MapReduce, Metlab “R” Autonomy, Vertica

Smart Devices Smart Apps Smart Grid

Clinical Trial, Morbidity and Actuarial Outcomes Market Sentiment and Price Curve Forecasting Horizon Scanning,, Tracking and Monitoring Weak Signal, Wild Card and Black Swan Event Forecasting

– Data Delivery and Consumption

News Feeds and Digital Media Global Internet Content Social Mapping Social Media Social CRM

– Data Discovery and Collection

– Analytics Engines - Hadoop

– Data Presentation and Display Excel Web Mobile

– Data Management Processes Data Audit Data Profile Data Quality Reporting Data Quality Improvement Data Extract, Transform, Load

– Performance Acceleration GPU’s – massive parallelism SSD’s – in-memory processing DBMS – ultra-fast data replication

– Data Management Tools DataFlux Embarcadero Informatica Talend

– Info. Management Tools Business Objects Cognos Hyperion Microstrategy

Biolap Jedox Sagent Polaris

Teradata SAP HANA Netezza (now IBM) Greenplum (now EMC2) Extreme Data xdg Zybert Gridbox

– Data Warehouse Appliances

Ab Initio Ascential Genio Orchestra

Load-Map-Shuffle-Reduce Process

Big Data Consumers

Load Map Shuffle Reduce

Key / Value Pairs Actionable Insights Data Provisioning Raw Data

Apache Hadoop / Big Data Component Stack

HDFS

MapReduce

Pig

Zookeeper

Hive

HBase

Oozie

Mahoot

Hadoop Distributed File System (HDFS)

Scalable Data Applications Framework

Procedural Language – abstracts low-level MapReduce operators

High-reliability distributed cluster co-ordination

Structured Data Access Management

Hadoop Database Management System

Job Management and Data Flow Co-ordination

Scalable Knowledge-base Framework

Hadoop / Big Data Management Related Component Stack

Informatica

Drill

Millwheel

Informatica Big Data Edition / Vibe Data Stream

Data Analysis Framework

Data Analytics on-the-fly + Extract – Transform – Load Framework

Flume

Sqoop

Scribe

Extract – Transform - Load

Extract – Transform - Load

Extract – Transform - Load

Talend Extract – Transform - Load

Pentaho Data Reporting on-the-fly + Extract – Transform – Load Framework

Big Data Storage Platforms

Autonomy

Vertica

MongoDB

Unstructured Data DBMS

Columnar DBMS

High-availability DBMS

CouchDB Couchbase Database Server for Big Data with NoSQL / Hadoop Integration

Pivotal Pivotal Big Data Suite – GreenPlum, GemFire, SQLFire, HAWQ

Cassandra Cassandra Distributed Database for Big Data with NoSQL and Hadoop Integration

NoSQL NoSQL Database for Oracle, SQL/Server, Couchbase etc.

Riak Basho Technologies Riak Big Data DBMS with NoSQL / Hadoop Integration

Big Data Analytics Engines and Appliances

Alpine

Karmasphere

Kognito

Alpine Data Studio - Advanced Big Data Analytics

Karmasphere Studio and Analyst – Hadoop Customer Analytics

Kognito In-memory Big Data Analytics MPP Platform

Skytree

Redis

Skytree Server Artificial Intelligence / Machine Learning Platform

Redis is an open source key-value database for AWS, Pivotal etc.

Teradata Teradata Appliance for Hadoop

Neo4j Crunchbase Neo4j - Graphical Database for Big Data

InfiniDB Columnar MPP open-source DB version hosted on GitHub

Big Data Analytics and Visualisation Platforms

Tableaux Tableaux - Big Data Visualisation Engine

Eclipse Symentec Eclipse - Big Data Visualisation

Mathematica Mathematical Expressions and Algorithms

StatGraphics Statistical Expressions and Algorithms

FastStats Numerical computation, visualization and programming toolset

MatLab

R

Data Acquisition and Analysis Application Development Toolkit

Statistical Programming / Algorithm Language

Revolution Revolution Analytics Framework and Library for “R”

Hadoop / Big Data Extended Infrastructure Stack

SSD Solid State Drive (SSD) – configured as cached memory / fast HDD

CUDA CUDA (Compute Unified Device Architecture)

GPGPU GPGPU (General Purpose Graphical Processing Unit Architecture)

IMDG IMDG (In-memory Data Grid – extended cached memory)

Vibe

Splunk

High Velocity / High Volume Machine / Automatic Data Streaming

High Velocity / High Volume Machine / Automatic Data Streaming

Ambari High-availability distributed cluster co-ordination

YARN Hadoop Resource Scheduling

Cloud-based Big-Data-as-a-Service (BDaaS) and Analytics

AWS Amazon Web Services (AWS) – Elastic MapReduce (EMR) Elastic Compute Cloud (ECC) and Simple Storage Service (S3)

1010 Data Big Data Discovery, Visualisation and Sharing Cloud Platform

SAP HANA SAP HANA Cloud - In-memory Big Data Analytics Appliance

Azure Microsoft Azure Data-as-a-Service (DaaS) and Analytics

Anomaly 42 Anomaly 42 Smart-Data-as-a-Service (SDaaS) and Analytics

Workday Workday Big-Data-as-a-Service (BDaaS) and Analytics

Google Cloud Google Cloud Platform – Cloud Storage, Compute Platform, Firebrand API Resource Framework

Apigee Apigee API Resource Framework

Hadoop Framework Distributions

FEATURE Hortonworks Cloudera MAPR Pivotal

Open Source Hadoop Library Yes Yes Yes Pivotal HD

Support Yes Yes Yes Yes

Professional Services Yes Yes Yes Yes

Catalogue Extensions Yes Yes Yes Yes

Management Extensions Yes Yes Yes

Architecture Extensions Yes Yes

Infrastructure Extensions Yes Yes

Library

Support

Services

Catalogue

Job Management

Library

Support

Services

Catalogue

Hortonworks Cloudera MAPR

Library

Support

Services

Catalogue

Job Management

Resilience

High Availability

Performance

Pivotal

Library

Support

Services

Catalogue

Job Management

Resilience

High Availability

Performance

Gartner Magic Quadrant for BI and Analytics Platforms

Data Warehouse Appliance / Real-time Analytics Engine

Manufacturer Server

Configuration Cached Memory

Server

Type

Software

Platform Cost (est.)

SAP HANA 32-node (4

Channels x 8 CPU)

1.3 Terabytes

SMP Proprietary $ 6,000,,000

Teradata 20-node (2

Channels x 10 CPU)

1 Terabyte

MPP Proprietary $ 1,000,000

Netezza

(now IBM)

20-node (2

Channels x 10 CPU)

1 Terabyte

MPP Proprietary $ 180,000

IBM ex5 (non-HANA

configuration)

32-node (4

Channels x 8 CPU)

1.3 Terabytes

SMP Proprietary $ 120,000

Greenplum (now

Pivotal)

20-node (2

Channels x 10 CPU)

1 Terabyte

MPP Open Source $ 20,000

XtremeData xdb

(BO BW)

20-node (2

Channels x 10 CPU)

1 Terabyte

MPP Open Source $ 18,000

Zybert Gridbox 48-node (4

Channels x 12 CPU)

20 Terabytes

SMP Open Source $ 60,000

Gartner Magic Quadrant for BI

Telco 2.0 “Big Data” Analytics Architecture

• SAP HANA is a new Database Appliance hosting a Hardware and Software bundle (SAP software powered by

INTEL core technologies with Veola Garda SSD In-memory Architecture). Introduced in late 2010 – HANA initially

focused on Real-time Analytics – processing vast quantities of data on the fly. SAP HANA now address many of

the challenges facing customers needing to make instant Management Decisions using very large data volumes.

• The SAP HANA Appliance was massively developed and further extended in 2012 to support the many upcoming

user requirements for processing Very Large Scale (VLS) data volumes in the realm of real time analytics. SAP

AG, together with INTEL, has expended massive effort in order to meet the emerging challenges of the Real-time

world – optimising Enterprise Resources in manufacturing, financial services, healthcare, national security, etc.

• SAP HANA presents a novel opportunity for businesses that needs instant access to Real-time Data for analytic

models that drive automated processing and Intelligent Agents / Alerts for instant decision-making. SAP HANA

also allows users to federate external data sources (ERP / CRM databases, message queues, Data Warehouse

Appliances, Real-time Data Feeds Internet Content and Click-stream Processing) with their Analytics Engines.

SAP HANA Overview

SAP HANA version 2

• Using Emerging Technologies such as in-memory Data Warehouse Appliances with

Real-time and Predictive Analytics Engines - we can now achieve so much more than

we could ever do before.....

• Real-time and Predictive Businesses are transforming the way that they think, plan and

operate. Based firmly on a foundation of In-Memory Computing technology, and an

extended Time dimension from Past (Historic) through Present (Real-time) into Future

(Predictive) Data - there is now a very new paradigm for enterprise information

management, which supports the three key business reporting requirements: -

DEVICE INFORMATION TIMELINE PURPOSE

Data Warehouse Appliances Historic Data Past Historic Reporting

Real-time Analytics Engines Current Data Present Real-time Analytics

Predictive Analytics Engines Forecast Data Future Predictive Analytics

MODELLING

HORIZON RESULTS

RANGE

(years) TIMELINE

DATA

TYPE FISCAL PERIOD AGGREGATION Financial

Management

Previous,

Current, Planned 5 - 7 Past, Present,

Future

Actual /

Forecast

Day, Week, Month,

Quarter, Annual Atomic and Cumulative

Strategic

Management

Previous,

Current, Planned 5 - 10 Past, Present,

Future

Actual /

Forecast

Day, Week, Month,

Quarter, Annual Atomic and Cumulative

Future

Management

Previous,

Current, Planned 50 - 100

Past, Present,

Future

Actual /

Forecast

Day, Week, Month,

Quarter, Annual Atomic and Cumulative

SAP HANA version 2 EXPERIENCE

SAP HANA Architecture Overview

SAP HANA Planning Methodology: - • Understand business opportunities and threats – Business Outcomes, Goals and Objectives

• Understand business challenges and issues – Business Drivers and Requirements

• Gather the evidence to quantify the impact of those issues – Business Case

• Quantify the business benefits of resolving the issues – Benefits Realisation

• Quantify the changes need to resolve the issues – Business Transformation

• Understand Stakeholder Management issues – Communication Strategy

• Understand organisational constraints – Organisational Impact Analysis

• Understand technology constraints – Technology Strategy

SAP HANA Delivery Methodology: - • Understand success management – Scope, Budget, Resources, Dependencies, Milestones, Timeline

• Understand achievement measures – Critical Success Factors / Key Performance Indicators / ROI

• Produce the outline supporting planning documentation - Business and Technology Roadmaps

• Complete the detailed supporting planning documentation – Programme and Project Plans

• Design the solution options to solve the challenges – Business and Solution Architectures

• Execute the preferred solution implementation – using Lean / Agile delivery techniques

• Report Actual Progress, Issues, Risks and Changes against Budget / Plan / Forecast

• Delivery, Implementation and Go-live !

SAP HANA Methodology

SAP HANA Architecture

APPLICATION CATEGORY VENDOR SAS SAP JEDOX

USER INTERFACE

Mobile Enterprise Application

Platforms

MEAPs Sybase Unwired Platform

(SUP)

Mobile Apps

Data Presentation & Display GUI SAS Add-In for Microsoft Office Enterprise Portal Excel, Web

Graphic Visualisation BLOBs Enterprise Guide, BI Dashboard,

SAS/Graph

PowerPoint

ENTERPRISE SERVER

Database Server Servers Base SAS Software SAP BW, BO, BI OLAP Server

Application Server Servers SAS Enterprise Business

Intelligence Server

HANA Accelerator

Data Warehouse Appliance Fast Data SAS Scalable Performance Data

Server (SPDS)

BW, BO, BI, HANA Accelerator

Analytics Engines Big Data Hadoop, “R” Hadoop, Pentaho

PERFORMANCE

ACCELERATION Massive Parallelism GPUs Accelerator

In-memory Processing SSDs HANA Accelerator

ENTERPRISE SOFTWARE

Data Analysis and Reporting Reporting SAS Enterprise Business

Intelligence Server

Crystal Reports / Business

Objects

OLAP Server /

Excel

Business Intelligence BI Base SAS Software BI / BO / BW OLAP Server

Information Management OLAP OLAP Cube Studio “R” OLAP Server

Statistical Analysis SAS/STAT, Stat Graphics

Data Mining Enterprise Miner, SAS/INSIGHT

Analytics SSM OLAP Server, SSAS

Financial Consolidation Controlling FI, CO, BPC / BHP OLAP Server

Enterprise Performance

Management

Planning SAS Strategy Management SEM / EPM OLAP Server

SAP HANA Applications

SAP HANA Hortonworks Real-time Big Data Architecture

• SAP HANA is a new Technology Appliance Coupled with Hardware and Software bundle (Intel

Architecture powered by SAP In memory Technology). Introduced in to the market late 2010, initially

focusing on Analyzing Huge volume of DATA in real time. It Address the whole challenge what

customers are facing with extreme volumes of data to make Management Decisions Quicker than

Never before.

• The Appliance has fine-tuned Very Aggressively in 2012 It meets most of the challenge in the Real-

time world. SAP to gether with INTEL, has deployed Huge resources to meet upcoming challenges in

the real time world. You may call it analysing your health, managing your resources, Prevention of

crime etc., Making us to run our live Happier Like Never Before.

• Data in real-time provides a completely unique capability for businesses that require instant access

to their information. In addition, SAP HANA allow users to federate external data sources (including

CEP engines, message queues, tick databases, traditional relational databases, and OData sources)

into their analytic models in order to further amplify the utility.